HONOR PTP-AN10
Geekbench ML Score
982
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | November 20 2024 02:17 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | HONOR PTP-AN10 |
| Model ID | HONOR PTP-AN10 |
| Motherboard | PTP |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | Qualcomm SM8750 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 81 architecture 8 variant 3 part 1 revision 4 |
| Base Frequency | 3.53 GHz |
| Cluster 1 | 6 Cores @ 3.53 GHz |
| Cluster 2 | 2 Cores @ 4.32 GHz |
| Memory Information | |
|---|---|
| Size | 10.93 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
427
80.0 IPS |
|
|
Image Classification (F16)
|
100% |
432
80.8 IPS |
|
|
Image Classification (I8)
|
97% |
1186
221.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
542
9.05 IPS |
|
|
Image Segmentation (F16)
|
100% |
542
9.06 IPS |
|
|
Image Segmentation (I8)
|
98% |
1080
18.0 IPS |
|
|
Pose Estimation (F32)
|
100% |
721
0.87 IPS |
|
|
Pose Estimation (F16)
|
100% |
719
0.87 IPS |
|
|
Pose Estimation (I8)
|
100% |
2976
3.60 IPS |
|
|
Object Detection (F32)
|
100% |
446
33.3 IPS |
|
|
Object Detection (F16)
|
100% |
448
33.4 IPS |
|
|
Object Detection (I8)
|
61% |
1329
99.2 IPS |
|
|
Face Detection (F32)
|
100% |
1056
12.6 IPS |
|
|
Face Detection (F16)
|
100% |
1046
12.4 IPS |
|
|
Face Detection (I8)
|
86% |
2585
30.7 IPS |
|
|
Depth Estimation (F32)
|
100% |
917
7.11 IPS |
|
|
Depth Estimation (F16)
|
100% |
911
7.06 IPS |
|
|
Depth Estimation (I8)
|
95% |
2758
21.4 IPS |
|
|
Style Transfer (F32)
|
100% |
1531
2.01 IPS |
|
|
Style Transfer (F16)
|
100% |
1537
2.02 IPS |
|
|
Style Transfer (I8)
|
98% |
4412
5.80 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
511
18.3 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
534
19.1 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2019
72.1 IPS |
|
|
Text Classification (F32)
|
100% |
640
919.9 IPS |
|
|
Text Classification (F16)
|
100% |
657
944.9 IPS |
|
|
Text Classification (I8)
|
92% |
1023
1.47 KIPS |
|
|
Machine Translation (F32)
|
100% |
1068
19.6 IPS |
|
|
Machine Translation (F16)
|
100% |
1066
19.6 IPS |
|
|
Machine Translation (I8)
|
64% |
1131
20.8 IPS |
